Cargando…

Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors

Positive diagnoses of gastric tumors from photodynamic diagnosis (PDD) images after the administration of 5-aminolevulinic acid are subjectively identified by expert endoscopists. Objective methods of tumor identification are needed to reduce potential misidentifications. We developed two methods to...

Descripción completa

Detalles Bibliográficos
Autores principales: Yamashita, Taro, Kurumi, Hiroki, Fujii, Masashi, Sakaguchi, Takuki, Hashimoto, Takeshi, Kinoshita, Hidehito, Kanda, Tsutomu, Onoyama, Takumi, Ikebuchi, Yuichiro, Yoshida, Akira, Kawaguchi, Koichiro, Yashima, Kazuo, Isomoto, Hajime
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9181250/
https://www.ncbi.nlm.nih.gov/pubmed/35683417
http://dx.doi.org/10.3390/jcm11113030
_version_ 1784723723579817984
author Yamashita, Taro
Kurumi, Hiroki
Fujii, Masashi
Sakaguchi, Takuki
Hashimoto, Takeshi
Kinoshita, Hidehito
Kanda, Tsutomu
Onoyama, Takumi
Ikebuchi, Yuichiro
Yoshida, Akira
Kawaguchi, Koichiro
Yashima, Kazuo
Isomoto, Hajime
author_facet Yamashita, Taro
Kurumi, Hiroki
Fujii, Masashi
Sakaguchi, Takuki
Hashimoto, Takeshi
Kinoshita, Hidehito
Kanda, Tsutomu
Onoyama, Takumi
Ikebuchi, Yuichiro
Yoshida, Akira
Kawaguchi, Koichiro
Yashima, Kazuo
Isomoto, Hajime
author_sort Yamashita, Taro
collection PubMed
description Positive diagnoses of gastric tumors from photodynamic diagnosis (PDD) images after the administration of 5-aminolevulinic acid are subjectively identified by expert endoscopists. Objective methods of tumor identification are needed to reduce potential misidentifications. We developed two methods to identify gastric tumors from PDD images. Method one was applied to segmented regions in the PDD endoscopic image to determine the region in LAB color space to be attributed to tumors using a multi-layer neural network. Method two aimed to diagnose tumors and determine regions in the PDD endoscopic image attributed to tumors using the convoluted neural network method. The efficiencies of diagnosing tumors were 77.8% (7/9) and 93.3% (14/15) for method one and method two, respectively. The efficiencies of determining tumor region defined as the ratio of the area were 35.7% (0.0–78.0) and 48.5% (3.0–89.1) for method one and method two, respectively. False-positive rates defined as the ratio of the area were 0.3% (0.0–2.0) and 3.8% (0.0–17.4) for method one and method two, respectively. Objective methods of determining tumor region in 5-aminolevulinic acid-based endoscopic PDD were developed by identifying regions in LAB color space attributed to tumors or by applying a method of convoluted neural network.
format Online
Article
Text
id pubmed-9181250
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91812502022-06-10 Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors Yamashita, Taro Kurumi, Hiroki Fujii, Masashi Sakaguchi, Takuki Hashimoto, Takeshi Kinoshita, Hidehito Kanda, Tsutomu Onoyama, Takumi Ikebuchi, Yuichiro Yoshida, Akira Kawaguchi, Koichiro Yashima, Kazuo Isomoto, Hajime J Clin Med Article Positive diagnoses of gastric tumors from photodynamic diagnosis (PDD) images after the administration of 5-aminolevulinic acid are subjectively identified by expert endoscopists. Objective methods of tumor identification are needed to reduce potential misidentifications. We developed two methods to identify gastric tumors from PDD images. Method one was applied to segmented regions in the PDD endoscopic image to determine the region in LAB color space to be attributed to tumors using a multi-layer neural network. Method two aimed to diagnose tumors and determine regions in the PDD endoscopic image attributed to tumors using the convoluted neural network method. The efficiencies of diagnosing tumors were 77.8% (7/9) and 93.3% (14/15) for method one and method two, respectively. The efficiencies of determining tumor region defined as the ratio of the area were 35.7% (0.0–78.0) and 48.5% (3.0–89.1) for method one and method two, respectively. False-positive rates defined as the ratio of the area were 0.3% (0.0–2.0) and 3.8% (0.0–17.4) for method one and method two, respectively. Objective methods of determining tumor region in 5-aminolevulinic acid-based endoscopic PDD were developed by identifying regions in LAB color space attributed to tumors or by applying a method of convoluted neural network. MDPI 2022-05-27 /pmc/articles/PMC9181250/ /pubmed/35683417 http://dx.doi.org/10.3390/jcm11113030 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yamashita, Taro
Kurumi, Hiroki
Fujii, Masashi
Sakaguchi, Takuki
Hashimoto, Takeshi
Kinoshita, Hidehito
Kanda, Tsutomu
Onoyama, Takumi
Ikebuchi, Yuichiro
Yoshida, Akira
Kawaguchi, Koichiro
Yashima, Kazuo
Isomoto, Hajime
Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors
title Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors
title_full Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors
title_fullStr Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors
title_full_unstemmed Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors
title_short Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors
title_sort objective methods of 5-aminolevulinic acid-based endoscopic photodynamic diagnosis using artificial intelligence for identification of gastric tumors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9181250/
https://www.ncbi.nlm.nih.gov/pubmed/35683417
http://dx.doi.org/10.3390/jcm11113030
work_keys_str_mv AT yamashitataro objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT kurumihiroki objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT fujiimasashi objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT sakaguchitakuki objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT hashimototakeshi objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT kinoshitahidehito objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT kandatsutomu objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT onoyamatakumi objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT ikebuchiyuichiro objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT yoshidaakira objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT kawaguchikoichiro objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT yashimakazuo objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors
AT isomotohajime objectivemethodsof5aminolevulinicacidbasedendoscopicphotodynamicdiagnosisusingartificialintelligenceforidentificationofgastrictumors